Texture classification using features derived from random field models

Rangasami L. Kashyap, Rama Chellappa, Alireza Khotanzad

Abstract: This paper presents a new feature extraction method for classifying a texture image into one of the l possible classes C i , i =1,…, l . It is assumed that the given M × M image characterized by a set of intensity levels, { y ( s 1 , S 2 ), 0≤ s s , s 2 ≤ M −1}, is a realization of an underlying random field model, known as the Simultaneous Autoregressive Model (SAR). This model is characterized by a set of parameters φ whose probability density function p i ( φ ), depends on the class to which the image belongs. First it is shown that the maximum likelihood estimate (M.L.E.) φ ∗ , of φ is an appropriate feature vector for classification purposes. The optimum Bayes classifier which minimizes the average probability of classification error, is then designed using φ ∗ . Finally the efficiency of the feature vector is demonstrated through experimental results obtained with some natural texture data and a simpler quadratic mean classifier.